Applications of Artificial Intelligence in Managerial Decision-Making for Sustainable Agro-Export Operations in Latin America: A Systematic Review and Bibliometric Analysis, 1996-2025

Background Artificial intelligence is transforming agro-export management by converting production, commercial, climate, and logistics data into support for more timely decisions. In Latin America, however, evidence on its managerial applications and contribution to sustainable operations remains fragmented. Objective To synthesize evidence on the application of artificial intelligence to managerial decision-making and its contribution to the efficiency, sustainability, traceability, resilience, and competitiveness of sustainable agro-export operations in Latin America from 1996 to 2025. Methods A systematic review and bibliometric analysis were conducted in accordance with PRISMA 2020. Scopus and Web of Science yielded 2160 records; after filtering, screening, and eligibility assessment, 73 studies were included. Bibliometrix in RStudio and VOSviewer were used to analyze scientific production, authors, countries, sources, Bradford’s and Lotka’s laws, keyword co-occurrence, and thematic clusters. Results Scientific output increased markedly after 2021 and peaked in 2024. Scopus accounted for 98.95% of the records. China led with 170 publications, while Brazil, with 149, was the only Latin American country among the five most productive. Four thematic areas were identified: prediction and classification using artificial intelligence and machine learning; logistics, supply chains, and suppliers; sustainable resource management and climate risks; and digital transformation through Agriculture 4.0/5.0. Across the literature, artificial intelligence was found to reduce uncertainty and improve decision-making, although its effectiveness depends on data quality, digital infrastructure, and organizational capabilities. Conclusions Artificial intelligence represents a strategic capability for strengthening sustainable agro-export operations. Its impact depends on integrating algorithms with reliable data, technological infrastructure, organizational capabilities, and sustainability criteria. The limited representation of Latin America highlights the need for further empirical research adapted to regional conditions.